class_docstr: General class for handling a Gaussian Process in GPyOpt.
  
  :param kernel: GPy kernel to use in the GP model.
  :param noise_var: value of the noise variance if known.
  :param exact_feval: whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  :param n_samples: number of MCMC samples.
  :param n_burnin: number of samples not used.
  :param subsample_interval: sub-sample interval in the MCMC.
  :param step_size: step-size in the MCMC.
  :param leapfrog_steps: ??
  :param verbose: print out the model messages (default, False).
  
  .. Note:: This model does MCMC over the hyperparameters.
docstr: Returns a 2D numpy array with the parameters of the model
docstr_tok: ["Returns","a","2D","numpy","array","with","the","parameters","of","the","model"]
code: def get_model_parameters(self):
      """
      Returns a 2D numpy array with the parameters of the model
      """
      return np.atleast_2d(self.model[:])
  
imports: import numpy
  import GPy
  from .base import BOModel
label_desc: Use image node_id 8 for calling the GPModel_MCMC obj's underlying member method code with example usage: obj.get_model_parameters() and returns: np